{"id":"W2800756927","doi":"10.1109/icassp.2018.8462371","title":"A Single-Channel Noise Reduction Filtering/Smoothing Technique in the Time Domain","year":2018,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"PESQ; Smoothing; Noise reduction; Computer science; Noise (video); Noise measurement; Algorithm; Signal-to-noise ratio (imaging); Reduction (mathematics); Wiener filter; Speech recognition; Speech enhancement; Mathematics; Artificial intelligence; Telecommunications; Computer vision","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006007932,0.00009172365,0.00007759914,0.0001086609,0.000149078,0.0002478951,0.0006371343,0.00004920191,0.00002800092],"category_scores_gemma":[0.00002992828,0.00006363513,0.00002677951,0.0004827822,0.00005859372,0.0005370785,0.0001268574,0.0001136851,0.00009687428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003953312,"about_ca_system_score_gemma":0.00002642431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002352718,"about_ca_topic_score_gemma":0.000005867795,"domain_scores_codex":[0.9991496,0.0000508767,0.00014601,0.0002569202,0.000166129,0.0002304375],"domain_scores_gemma":[0.9995168,0.00002680898,0.00004924497,0.0003398994,0.0000386873,0.00002851505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005679943,0.00006883663,0.000007169614,0.00000802815,0.000002223841,0.00001416051,0.004043404,0.000008060523,0.9590697,0.0007092089,0.001822703,0.03424077],"study_design_scores_gemma":[0.0001474243,0.0001449263,0.00007044903,0.00007818815,0.000001229382,0.0002568318,0.0001695642,0.002602432,0.9668838,0.02701269,0.002451895,0.0001805012],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04974317,0.0000207561,0.9201903,0.003249732,0.0001643692,0.0002000679,1.799231e-7,0.0002552342,0.02617613],"genre_scores_gemma":[0.6953111,8.759426e-7,0.3031076,0.0008150823,0.0002548367,0.00003161285,4.932698e-7,0.00000774401,0.000470677],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6455679,"threshold_uncertainty_score":0.2594965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0172313016354818,"score_gpt":0.2380542270513137,"score_spread":0.2208229254158319,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}